{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "from sklearn.metrics import classification_report\n",
    "from sklearn import tree"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 载入数据\n",
    "data = np.genfromtxt(\"LR-testSet.csv\", delimiter=\",\")\n",
    "x_data = data[:,:-1]\n",
    "y_data = data[:,-1]\n",
    "\n",
    "# x取所有行第0列，y取所有行第1列，根据y区分颜色\n",
    "plt.scatter(x_data[:,0],x_data[:,1],c=y_data) \n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,\n",
       "            max_features=None, max_leaf_nodes=None,\n",
       "            min_impurity_decrease=0.0, min_impurity_split=None,\n",
       "            min_samples_leaf=1, min_samples_split=2,\n",
       "            min_weight_fraction_leaf=0.0, presort=False, random_state=None,\n",
       "            splitter='best')"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 创建决策树模型\n",
    "model = tree.DecisionTreeClassifier()\n",
    "# 输入数据建立模型\n",
    "model.fit(x_data, y_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 导出决策树\n",
    "import graphviz # http://www.graphviz.org/\n",
    "\n",
    "dot_data = tree.export_graphviz(model, \n",
    "                                out_file = None, \n",
    "                                feature_names = ['x','y'],\n",
    "                                class_names = ['label0','label1'],\n",
    "                                filled = True,\n",
    "                                rounded = True,\n",
    "                                special_characters = True)\n",
    "graph = graphviz.Source(dot_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
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       "<text text-anchor=\"start\" x=\"136\" y=\"-260.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">samples = 5</text>\r\n",
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       "<g id=\"node8\" class=\"node\"><title>7</title>\r\n",
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       "<text text-anchor=\"start\" x=\"270\" y=\"-290.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">y ≤ 7.32</text>\r\n",
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       "<text text-anchor=\"start\" x=\"254.5\" y=\"-260.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">samples = 10</text>\r\n",
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       "<text text-anchor=\"start\" x=\"23\" y=\"-164.3\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">gini = 0.0</text>\r\n",
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       "<text text-anchor=\"start\" x=\"382\" y=\"-141.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">samples = 4</text>\r\n",
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       "<text text-anchor=\"start\" x=\"330\" y=\"-52.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">gini = 0.0</text>\r\n",
       "<text text-anchor=\"start\" x=\"320\" y=\"-37.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">samples = 1</text>\r\n",
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       "<text text-anchor=\"start\" x=\"453\" y=\"-52.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">gini = 0.0</text>\r\n",
       "<text text-anchor=\"start\" x=\"443\" y=\"-37.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">samples = 3</text>\r\n",
       "<text text-anchor=\"start\" x=\"441\" y=\"-22.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">value = [0, 3]</text>\r\n",
       "<text text-anchor=\"start\" x=\"438\" y=\"-7.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">class = label1</text>\r\n",
       "</g>\r\n",
       "<!-- 9&#45;&gt;11 -->\r\n",
       "<g id=\"edge11\" class=\"edge\"><title>9&#45;&gt;11</title>\r\n",
       "<path fill=\"none\" stroke=\"black\" d=\"M444.214,-103.726C449.041,-95.0615 454.147,-85.8962 459.002,-77.1802\"/>\r\n",
       "<polygon fill=\"black\" stroke=\"black\" points=\"462.14,-78.7389 463.95,-68.2996 456.025,-75.3322 462.14,-78.7389\"/>\r\n",
       "</g>\r\n",
       "</g>\r\n",
       "</svg>\r\n"
      ],
      "text/plain": [
       "<graphviz.files.Source at 0x22e0f5a7358>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "graph"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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9wH9T1fuA/5Y93oeInAbeD7wWeA3w/iJDYRgNxHv8uXO4hXnc7AxuYT5t09iBfHZpN0m3XL9tv+KcsYhIvpESwXfCeCUJoVod3obwbXYi4gZjlzIuHMkAqOpngJWWw28D/mP2838E/k7OS/9H4AlVXVHVG8ATHDQkhnEAEcFNTuJmZ3GTkx11Y0hBQLW1wtjNzh6ctCRLJS2YsNzkJP7MmTSFM+uF7BfPnEjATVVJbtwgubpEWLlBsnSNZGVl6PL727nwioLqRnfoRBD4vKq+CKCqL4rIuZxzLgLPNz2+nB0zjL7hp6dIBHR9PXXleL9raJqRUilVxlxbS5vPNNQwb9EwRiplosqZwudVFa1W0c2tVKV0chKZKjZyYWMjzfNPX5x+26mmabFDFDwV73ELC6k+UuN3VUUW5tv2bzY6T6+ygHLVWHJPFHkEeARgwt9a68UwToKfmoJDdP6ScplocbGj9w5ra+jW9u5kHup12N5KG9TkGIHczCJIg6ddUjbdjZU4d+D6mgnSaZKkdQ1HaKHppiaRicpuqm9Ry06ju3TCAFwVkduy1f9twFLOOZeB1zc9vgP473kXU9XHgMcA5svnh2tvO2ZoEtCd7bSwqVyBcmlgMk0GHY3jgxO6KtTqJDdu4mdmDvYsaJNBpCF0XOsn2dpC19ayQrhUfbUhoa1JkvZfaATBVdPK5oX5Q/8PiHNptpLRNzphcj8KNLJ63gn8fs45nwLeLCKnsuDvm7NjxpASqlWSpSXC6hphfYNkZYWwcmPo/NH9Qmu14mDozg7J9WWSm6v73882efzh2nJHg8Jhp5rKaDepierWFmF1DYDkxo00O6pZG2l7O5WwNoaGo6aBfgT4c+DlInJZRH4K+GXgTSLyDeBN2WNE5AER+Q0AVV0Bfgn4bPb1i9kxYwhRVcLKjf357qporbZX/Wq051bujqxngdZqey+ZnS3sbUwIaeewDhHW13OdtLq1RajHhb0UitxUxmByJBeQqr6j4Kk35Jz7JPCPmh5/CPjQkUZnDCZFxTqqhK3tWwZHjfY9indRTQ1qpUKysZEVrrU5v1prr2l0FIp0g0TQei3/OWhfBGcMHBZ1MYw+ICL4xTO33gkAYWcnzVTq4eSa1zO5gbbpYlZUvW0MJmYAjKNTKhW7IkRIrq+QrG8crWtWD0g7eu2QrNwgWbmRTqxdnlQ1ZP0MNrcO+OilVMKfP4dro2zqJqcIG7dY+TeuNznRsSB8fu2DwNRk22C0m7HMvWHCxOCMIyMi+FOnSRpNzpsn0Wo1nauq1Vs2Pe81YXV1n16+VqvIRAV/qjtF6fu7iCloWjzWXA2cah5NwKkFwo39fYNlejpVIr15C0O6K5HRuVoAKZXwi2cIa+tpHMJ73OwM4j3JVkGcJ4rS5jfG0GAGwDgWUinjz59LJ9SGTn3ryjAEwtpa1ybYo6D1eppzv++gpj11p2sdV8rc10Ws+fjWJmGigmuJAbjJSaTc1LOgMrErQSGVCpqXXSOCzM6kOfhdkFJuFMC1/l6F509aSuewYebaODbiXNqUZHqq0C0wKF2o2o2jXcevY9+vVhAo1eJMGfEeNz2Nm5nZpz/kZmdy3TEyN4efmcFVKj2rvxARZH7uoAvQe1yb9pbGYGI7AKMDtJl8BkXcq904pAvroHaxhYYLShXd2UFrdSTyyORkbjVsKop3NpWCqNbS5jYz0wd2Eb3CT02hpVK660sCVCppZa9V8g4dZgCMEyNO9pX173tuqnvNT46CTEzC2lpuMFUmO9NfYN81i1xKIsjUZNq8fXk5nUBVURFYWy8UjBPv8R308Z8UKZXwOb2WjeHCTLbREdzCQtpysLnp+cQEbmZADIB3uFOn9saXfbnTp7oSpBbnUlfJvoOS+uonJtJCq0YlLexW1CYtgWDD6Ca2AzA6gjiHX1yEeowm8ZHFwXqBm5hALpzf9flLudJV/Xk/NYWWy6k8QtBU8Czz12tR45M4TsXVBiRzysgnigLOK7XqcP+dBusTagw1IpIKwtH9rlfHZTftslf3iyL8XF5zmHYvGpC4iXGAufkaP/u/fpnv+VvXcAJf/+o8v/ZLr+L5b870e2jHQgZZvOuVry7rb3/sfL+HYbTwWze+n6/8bfu7NNh+1UV+7bEPHOk1n/7Vi/zRo7cT7+ytIMUF7vruTX72977S9rUCnPMRp5wHhNUQczWJGayyu9HkvqhCWQSXGWlVJQBP13coEM/oOffffflzqvrAYc61GIBh9IEf/Okr3PXqTcpTCb4cqEzHzJ6N+Qe/+swtX3tvVGbRRZTEURLhtIt4WWQSDN1mOnu/XdMOTUQQYMENpyvIXECGcQs0UzpF0wK4TuTclyaUn/ndr3LpyVme/9I0py5WecUbbuJL7Xfk0+KYELdvEnKSOt3mxLGmtg/oFuWCv7sTYUIcDMwe4PCYATCMNoRqNZW+bsKdWsB1II4gAvd+zzr3fs/6oV8zKS43fOBFmHSOtcQMQLfYKTCuiSrbQ2p4zQVkGAVoCHt9D5q+wsoNtEguucvUNOTqwiWq1AY4njcKbGcTfWh6n9MYgHIzDN/qH8wAGEYhDV2e3Of61PhmXQMBDkxCCqwO6SQ0TFyKayyHmFiVRNOJ/5l6dWgD8OYCMowibtGDtx8o8Gy9yp1Ricls/bajgctxfWgmIQHmnWdaHDVVboSYzjWz7C4KXE3SrKtRwAyAYRSQdu3K8c+L9E2HB6CO8lxc292+D8vED6nL4WWlCiUEL0JQ5ayPuBTX2BpSP/owYwbAMAqQUinV7WnqIYCkmUDtGrT3ipNOl1eemuLxX7iHS5+bpTwZeO3DV3nonz9PVOleLOGsjyizl0rZ+H5nVOLp+mAox44TJ44BiMjLReQLTV9rIvJzLee8XkRWm875hZPe1zB6gZufx51aSCUcKhXcwgLu1KmeyS93ixsvlPn1t7+Cb352Fg1CddPzZx8+z2+9+76u3nfe+X0prA0ihFLb8mijG5x4B6CqTwP3A4iIB14AHs859U9U9S0nvZ9h9JJd6Ygeykf0gj/50AXimqNZkyLe8Tz9mQWuf7vCmbu6sxpXpVAGQw/T99LoKJ12Ab0BeFZVv9Xh6xrGwDL55Rd4z/f/aL+HcSTi5etQP9i0Jq55/tXf+aGO1Dnk8SN//xL/8Ge/zsTkngMrieEbTy3wz37i+7pyz/Hj3x76zE6ngT4MfKTgue8TkS+KyB+IyCuLLiAij4jIkyLy5I0VCwqNI3uN1DcPNFI3OkNzx7F9qHZVxfXjv3cXn/3Ts1R3HNtbjq1Nz/LSBL/83vu7dk+jmI79pUWkDLwVeG/O058H7lbVDRF5CPivQK6zUVUfAx6DVAyuU+MzhoN9jdSzwKtMTeHm54be734cZuZq/K03vsTsXJ0v/NUi3/hqZ5rCuOmZtLl7S52DTFS6agBC4vjl9343d33HOi9/1SrLVyf44mfPEML4/W0HgU7+pX8Y+LyqXm19QlXXmn7+hIj8OxFZVNXlDt7fGHJUlaRRedt8fHsbnaj0VMb5KBxHDfQwTIvj7ijNNnLAj7/r66yHhOeTekeuf+WpKR5//z1cerJ3WUB5/FhP7zb6fPLuw5/bSQPwDgrcPyJyAbiqqioiryH9f77ewXsbt2BKHA7YLJASGAhq9fzKW9W0kfqAGoBucVdUxjftejww6zzzGo5U9TsrjnM+oiyObQ1cTepsq3L739jiXf/5q10YuTEsdMQAiMgU8CbgHzcd+2kAVX0UeDvwMyISA9vAwzrIjQhGiAkR7okqu8EeAV5I6gOpXdIuC2RU/l3OOM9ZH+ERtjXwYjYZtzLVRvRtQTyrh1SeXHCei760m3o5g2M6qvBcXM29rzFedMQAqOoWcKbl2KNNP38A6Pwe2WiLAPdGFaIW3/lFX2JbA9UBmwAKG6kDbmqyhyPpDhd8xGkX7a7qp8XzHeJ4Nq6yc4S/xVFCIbc1Tf7pawVUOe9LXIoPZgEZ44WJwY0wMwWrSIGsm9RgISJpc/n9B6FcRiaH2wA44EzT5N9AgLPu4Dpsq43q541D7t48+R9wEWFK7KNvmBTESNM62TQQEaIBrbp0kxNI6SxhaxtC2NdI/aRorUbY2Eybrk9UcNPTiOvNRFgSyZ3QJdPxz/PofDuucXdURkgNRQDWQ3Jo/3+7JOpxlY4WGNwYWB8wAzDCbIQE8QcbtCeqrA1gDKBB2kh9tqPXTLa20Juru4+1XifZ3MKfXUR893dDddVck6uqVEP+lLSpgafrO8w7j0fY0ORIfnsFrod4n9sJUinppQ5lEg0Lp5znvC8Rkdraq0mdlQH+DPQK2weOMDFwLYlJVHeDqIkqOxrGqnWgqqKrawefCIGwsdmTMQTgRkhIWlNcgaVQPBknwEpIuBbiYwVtX0piVkJMUCWoEqtyJamP1d9/wXlu9yVKIunuV4QLvjSQbtBeYzuAEWcpxGxq4LTzeBFuHsGFMDK0qSbWnR2Yn+vJMK4kdWJVFn2EA6rZZNztbJyXMv16D0Oju99JzvvogACdF+G8Lx06njKqmAEYAzY1sDnOvWKdK+zsRZdjAHNZE/cdDaxrYCnELIXeT8PKeE7+QKHKqE1+9h4YfULrdcL2Tqq2OTnRVfkB8R7KpbTQbN8T4Gamu3JPT9r4JEJwpC6gBOXZenWgJuIKwm1RiRlxmZsq5qUkHqlAaVWViZwkgvpI/ZbHw2IARs9JVtdIlpfRjQ3C+jrJ0jWSze764v2pU1AqpWml2WQg0zNdk5e43Zd2u16JpN9LCLfnBOX7RURqpGbE7Y7xtIt25SdGhZeS+r4eypAGwl8akbaOJ8F2AEZP0VodzZnsdXUNnZjoWkaOeE90dhGt19EQ0m5fXXT/zOU0PhER5pyHlgycRec560t4YEeVF5M6mz0I0p52aTJwc4qtE2EaRwWhOiIr5HUNfDuucd6XqIhQU+XqmAXCizADYPSUsL2d/4QIulNFpqe6en8pFfed8j4wO19nfbVEkvRmc3zeRZzxe2makyLcI+WeSDVMisvtzqVARWTgKsVPwroG1mNrOdmKGQDjyPz4qT+DPzveaz/xf97JHz96O9oi/1uejHnbez/Pax++1oERHp1FF3HOR7uFQktJzPIJgrVrITmwC9CW+guBNCMopzr4nCvxraS7Ug2BND24tchOYGRW/0Z7LAZg9JT733KdqHxw6x0S4RVvvNGHEcFp5zmXrcJd5gs/5yNOnyBP/EpSp47u1mAkqtRJ0z4blCiuDp5w3a3UnszcUa2Tv6qyOYA6UUZ3MANg9JTbX7HFG971AlElwZcDUSUhqgTe/n88x+xif4Jy53zpgGxGwwgclwT4er3K5bjGUhJzOa7x9ZYMoDptqoO7PAEvumIxkBfj7lcJD6YQyfhhLiCj57zxZ69w/49c5yt/eApfUv7mgyvMn++fNEHRh6ATeklrbaquFVgO8QGROCWVKugmjarYVgIQddH/f9ZFnM0K4eooV+I66xaM7RtmAIy+sHhPlR/4Ry/1exhAcZ54L9wgV5OYoGkswNO76uBNDUzqwSCwANtdmpDPu2hfzKOMcFdU5lJc60nWk3EQMwDG2PNSUucuKe+bDEOWjtkLroWYaz2uDl5OUpE4VHd/70SV5SRuqyJ6XAQ4kxPwdiKc9xHPWW+CvmAGwBh71jVwKa5x3kdUxFHVwNUkHulVaQJ8o77DOV9i1jliVZa7qBMVIYUOtYr1JugbZgAMg9QlMm6r0Jg0W+mQ3SUPxaQIQQ+mkcYFDT9V9ZYup7SxjZh0QxfomAEQkUvAOum/U6yqD7Q8L8CvAQ8BW8A/VNXPd+r+hmF0lhLCtHMEVdYLOpQ1mBPHxabmNTVVvhXXqGWvatRWtCpztgt4R8BdUZnJbIcQozwf19ka4Z1Zr+n0DuAHVXW54LkfBu7Lvl4L/Pvsu2EYA8a5LFunedL/ZkF1cgXhzmh/DKUC3Fsq83R9r/p2OcQkKOd8RISwo4EXk+I+B99RqlBmL1upjHBPVOYb9artBjpEL11AbwP+k6adSf5CRBZE5DZVfbGHYzCMY+NIfdn1AnfGqDAtLrdC+Z6owlP1nQPnn/b+gH9fRPCaXqs5lnIjJIfS4J8Sl8YNcrKUTntU+byRAAAZGUlEQVTPVRNy6widNAAKfFpEFPgPqvpYy/MXgeebHl/OjpkBMIDU5VCSdGU4SJt8Ae7wJeac3534XxrhloKnnc9vJs/BCR2ghCvs2RyJHKsJb7ngek6EspWRdYxOGoDXqeoVETkHPCEiX1PVzzQ9n1v02HpARB4BHgG47aK1bBsHHKmvd1ocSvqPshzigVnlNSb/5hXxBV+invnGRw3JWXk3yDMM65owo+5ANbUAW+F4789WCEjOxz/JpCqMztCx/CtVvZJ9XwIeB17Tcspl4M6mx3cAV3Ku85iqPqCqD5w6belh48BFX2I6U6Zs6PGccRELA9Cz1ZEv7exFOHsCqYhBZlUP9i6GdELPm3xvhoRa1nO4QaLK9RAf21dfIxXOa75mUCVBx76NYyfpyAwrItMiMtv4GXgz8OWW0z4K/C+S8r3Aqvn/jXYT7KI7+QQrnGybGxUItkEqpzCKrIaELQ27RkCzyf2FpJ7rmlPgubjK1SRmOwQ2QsLluHbihivPJ3VeTOrshEBNAysh5pm66ZR2kk4tYc4Dj2fbxgj4bVX9pIj8NICqPgp8gjQF9BnSNNCf6NC9jSGm3QokOuEE29Dab0g8X03qXD/i6rFWMN0E1WO7N4aBS3GNWXHMOk+CcjNJ2kpEB1K33UkktPNYCcnIxloGgY4YAFV9DviunOOPNv2swLs6cT9jdIhJe+W6lhCRqrJxgg/+2ZZGK5D67RNSl8VReDGpc7sv7e5SgmpmUAYjRtEt1jWwnoyukTNMDtoYAF6I056tmrkcUl/vySbYsy2TP6QZJMeReL4REr4V19gICTUNrIaEZ+rVwt2BYQwLoxnFMoaKdQ08F1dZdKkWz6YmXEtijjv9C8Urm+KGkO3Z0MDGmElFGKOPGQBjINhW5fkOqW8qqWzAcSf7VqbF7ROKeymJTY7AGAnMBWSMJDthz6XUjJJO6IdlVhz3RGWmnScSYdp57o3KzJiCpTEC2H+xMZIEKCxmOkp1wW1RKVfD/oIvHX9whjEgmAEwRpJA/g7AkV/MVESRVn1eBzHDGDbMABgjyaQU69MchbhAqTK2DCBjBLAgsDGSuIK5X0mbiySHnMCvJXXO+9KBdpHdqgEokUpMzIijTtqicRT1hozBwAyAMZKshYTTTg7472P0SPo0yyFBskm5UVG8lNS7okdTQvjOUgVPGr+okMoivzjCyqNGfzEDYIwkS0nMnPN4TXWFGtW7l+OjpZpOZ6mfT9d3EDh2bcJhOOuj3cm/QSPgfCMcds9iGIfHDIAxkqRNz6ssOM+0OGooK0ly6NV/RYR7o8pukExItYSWu7gSn24Tt6iIsFMQjxg1IuC0iyiLsKmBm2b8uoYZAGNkCWRiYsfoen5vVCFi/2r8vC+xpdq1IrCiwLJQHIzuBgKc9xELLnV7rYaEq0m9k73jC5nMDK+Q7n7mNG0h+Uy92pP7jxuWBWQYLUyJw3GwjkBIu2V1i2tJvE//HtKA80YIXXU9tXJvVOaMiyiJEIlwynleVqr0pA/XHb682xMCUvddhHDO6i66ghkAw2ihaIoXEQ52v+0cGxq4ktRJVEkyDf6NEHg+6Z0G0bQ4JrLmPA1cNgnPdblBjye/FaQTYX4AmgONIuYCMowWNjXkTvOJKqua74hwpH7rOeeoq3I9a6pyVG6EhJshoSxCnKmi9pIJkdzf3YswKcJqF+/dzsmlFgXoCmYADKOFAFzJegAI6co/UWUnk4JuxQH3lSpEpK4LVWXOea4cM11UgWqfAr41zZ9qE1VqXR5TIDW+My1N5oMq1xOLAHQDMwBGLiXSbbcjbfq9PSYZKA1uhIQdDZx2ER5hTRNWC7JRFl20O/lDajAEuN2XmHep06ixsu81Asw7z6QIVVVuhiS3rWODdQ1pgx7di4FoZhR6Mf7LcY3vKFWIsjdagI0QOt5pzEgxA2AcYE4cd0ZlIP0AniXiRki40iG55mFhO+uDeytmc3oaQ/rezWa+60lxzDvPt7rYU8ABE+KIVamheMgKywSf7WLO+xLP3qKZzbP1KndEZWayEOGWhrRpT9dGvkcMfL1eZVocJRG2NfRtNzQOnNgAiMidwH8CLpDu4h5T1V9rOef1wO8D38wO/RdV/cWT3tvoPA64Myrvm9AEWHCe1ZAcSUhtXChK0Wx2Y3gRpnFMi+vKe7joPOd9CSX9e+1kE2fzzsRn7qmLUYlvtjFEMWlPYMmu1Y+/+KaG9kEBoyN0YgcQA/9UVT8vIrPA50TkCVX9ast5f6Kqb+nA/YwuMiMu93PnSI3ApvWIPcD1EDPj9mfOqOqBNFIHzDjX8fdwRhznWvSKJnFMSk4qa2aIGrIW7dBDnGMMNydOA1XVF1X189nP68BTwMWTXtcwhoUNDVxN0r7GjfTNPBRIujCjLub0P+6EEqox+nS0DkBE7gG+G/jLnKe/T0S+KCJ/ICKv7OR9jc6xUZACGehNEHBYWQ4JT9V3+FZc47m4Wug2udmFYGZUUJugcMAYqSrrIZxoZW+mZXToWBBYRGaA3wN+TlXXWp7+PHC3qm6IyEPAfwXuK7jOI8AjALddtOKPXhOAb8c17orKu/5kgJUQm///FjTSGCH1od8dlfdNlt+Oa12p6F3XhIoeVD5N00kDlSaXT4zywjELy+bEcVtUooQQSCuXr1l2zlAjeV2TjnwRkRLwMeBTqvqvD3H+JeABVV1ud94rX13W3/7Y+ROPzzg6HrI0UGE9JFTNG3wspmQvk6ZbeNI6BN9Uh6CwW4eQVvemaaAbxxzHjDjubkkOCKpcS2KWzAgMFPfffflzqvrAYc7tRBaQAB8Eniqa/EXkAnBVVVVEXkPqerp+0nsb3SMB06DvAJ2c+CsIiz5iQhzbGriWxNTRXeXTMy5i1jnqCssh3r33pgY2T2i/z/sotzfyoo/MAAwxnXABvQ74ceCvReQL2bF/AdwFoKqPAm8HfkZEYmAbeFg7sfUwjDFhShz3ZrUZToRJFRac59m4SjWTjFgKMUtd2miUC3ojC+kOxJYKw8mJDYCq/im3iAup6geAD5z0XoYxrlxsSfMUEZwqt/kSl7pYXNagqoFIDsbkAjb5DzNWCWwMHQ3htQXnSVCuJzFrIxygFtKGMAeOZzn9veClJObeFpXQRJWlMasOHzXMABhDhQAviyqUmzTjJ8WxEmJe6lKj9n7TKMjKVSg9YnA+As74iClx7Ghg+ZBd0rY0cCmucSGLQdRRlpLYUoOHHDMAxlCx4Dwl2Z/y6EU44yKWk7injVN6yUqIOe2iA1k4R1HJLGdN5xvdtqbUccpFfDOuHkrsb1MDz/bA3WT0DmsIYwwVs+IPVL1CukKeKghUjgIvJTFrIdlXbXwjJEfKw7/Nl3Cwa0ScpCJxF325S6M2Bh3bARhDRZ2Aan7z9FFd/UNq4J5P6kRJnbI4qhqOHHydcfnvW6MJjKXljR9mAIyhYiVJOO32ix+oKglHa9Y+LY5TWaHbaqb1PwzEQHzMgHdC/pbfRN/GFzMAxlBRRXk+rnExk1kQ0i5WR0mFPOciFn202/h9RlNj0It0yn6yksSc9QfjCBbIHV/MABhDx5oG1uo7TIgQlLbNTVqJ4MAk6EWYwjErjvURTiddCjEVSZu7N7KKNrNG9MZ4YgbAGFp2jlFMPpNNfq34bGJc71G/g1lxRFnHq+P8Hsfl+aROKYl3tYGOYjyN0cMMgDFWJEVa/VkcoduUEF5WKuOQ3TjGekj4dg9X4XWUuimxGFgaqDFmbGi+Fr6SNm7vNndF5d0evS77mnGeM86kz43eYwbAGCsUuBRXibN8+kZO/QtJvevNxyPSlMtWVU0vwmlnm3Gj99h/nTF2bKvyVH2HaXE40kBoLzz/0kYzcZA6OAppmmwjSDy6YXHDDIAxcEyJY0ocNQ2sF7hsOkEnO5xVsoykdro6dZQYpdxiCILqwNQhTGWNXxojFOCFpG6poiOKGQBjYBDgnqjMpOy1MAzAs/XqoQTL+sGcuH01CTsa+Fab1o/Px3Xuyc53IiSaBmSvDYCQXeP9b5XauOhLbIdgXeFGEIsBGAPDWZeqVDYCpF6ECLgrKvV7aLlURLgzKhPJXlB3Uhz3RpXC12xp4Ov1Ha4mMdeTmBeSOs+0aSLfS2bbNH1Z8BakHkVsB2AMDKe8PxAgFREmcAPZdepMTuBWRCgBkyKFCpsxacvGQSNPZA/S38m37/lkDClmAIx9zInjvC9REmFHAy8lcVcbmg8zZQ5m9DSIhlBebSMEcpp+kaiybjGAkaQjLiAReVBEnhaRZ0TkPTnPV0Tkd7Pn/1JE7unEfY3Ocsp57ozKTLjUDTPtPPdG5Y7ILB9m/XgzkztuRlV3e94OGut6cLyQ/q7bOUZz3nleXqrwqtIELy9VmB+w3P86aSwiUaXRsjtRZTMLxhujx4l3ACLigV8H3gRcBj4rIh9V1a82nfZTwA1V/U4ReRj4V8CPnfTeRme50NJ3FtJA5Xkf8c1jCKUJcLsvseB8FiBVXkhqha6Ra0nMrHjKpO6IRDWTQR5MkbYbIWHRR0S6p7GfqLISDjammXeeO5re3zLCHT6NbQxKBhCkekGbGnaVUm+G0W63Oe50wgX0GuAZVX0OQER+B3gb0GwA3gb8y+zn/xf4gIiIqtWjDwqe4u3g5DF3AHdH5TTXfrd1o3CvVPhGQVZPAJ6Jq8yJY9I56plS5aBOPwH4Rr3KWR8xJ1l/4pAvLX2hRYAOUqNxwUcDZQAgTY/d7JEmktFfOmEALgLPNz2+DLy26BxVjUVkFTgDLHfg/kYHSCj2WNeOYafLyL7Jv4EAi97zYpu0xzUNrA3JBBSAq0nM1Vu0oykVOMGKjhtGL+hEDCDvP7h1xjjMOemJIo+IyJMi8uSNleGYBEaF5cz/20xQZekYQmVlkdw/sBNhYoRbNxZRVMcwqPUNxnjQiU/iZeDOpsd3AFeKzhGRCJgHVvIupqqPqeoDqvrAqdPjN1H0k6UQ7xqBoEqcaeQcxwdcVc21+kGP1rlrVHgpiQ8EjIMqLw1AAZgxvnTCBfRZ4D4RuRd4AXgY+J9azvko8E7gz4G3A39k/v/BZCnELIUYByfyvddR1kLCnNvL7dcsqHt9DCe9hp//go8oIdRJJ/9B8/8b48WJDUDm03838CnSWOKHVPUrIvKLwJOq+lHgg8BvicgzpCv/h096X6O7nHSNvuA8s1maY8PWb4S0+9T4Tf8pqwUBYsPoFx0pBFPVTwCfaDn2C00/7wB/rxP3MgafCREutqSUqioVJ9SS8d74TYow6zyqqUGwjlxGP7FKYKPjFEkkeE3VJscxBgBwm4847fZqhM/5iCtJvSeNaAwjD4uyGh0naiuRMJ5MieO0S2sBpKkb2O2+xGDVAxvjhBkAo+OsaZLbe7fRYGQcmXOuMBd6dsAkIYzxwQyA0XFuhoRalkraIFFlKYkHUtPHMMaVcd2RG11EgWfjKqedZ955EoXrIWZjTFf/kBrFM5n/vxkB1iwGYPQJMwBGV1Dgeki4bpMbkArhLSUx53z6kVPSyf/5uDawWkfG6GMGwDB6xLUQczMkzDlHIF35m3k0+okZAGPkEWAm0x/a1NDXFXc9Uww1jEHADIAx0syI466ovPu44XYxjXvDsCygI1HJ8rbvicosusjevAHHk/Yk8FnT9kbj9jujsskwGwa2Azg0s00rSSfClDjOeM8z9ar5cQeUOecLhRbmnT9SY/YIuN2XmXOp2V8PgReS2tjqGhmjgS1iD8kdUXm3ehPSloURwllvNnRQmRTJ/QcXwB1xA/CyUiUt5soqeWed42Wliu0jjKHGDMAhKCO5H3QnwpxVcQ4kDjjlIiRHkkJJlUkPy5w4PLLvWiKCx/7+xnBjBuAQBPKbmwC5kgdG/5ktcP+oKrUjNqWpiMv9oDjSuJBhDCtmAA5BDGxroLWHTaKW0jeotJuWj6pHtFOQOhqAnSPsJAxj0DAH9iH5dlzjnqhCI6FQgJWssMcYPNZDgvjSgeOBVId/3nkWJHXf3Ahx27TQdQ3UUUTZjQEFVWKUdUsnNYYYMwCHJAaeiatMiFBC2NZgGSADTAK8mNS5ze8lfDaqb884z4zz+Gwyn3aO1ZDwQlIvvN5z9SoXfImFzOe/GhJeTKyluzHcmAE4Ijuq7NjHvqtI9nXStfVKSNgMgQXvccBa5q5p1AY08CIsZGmh1YKYTgK8kNTbGgnDGDbMABgDgwNu9yXms1V2HeWFuH6iHgJVlKtNTejP++ICvhlxVNVcesb4cCIDICK/AvwIUAOeBX5CVW/mnHcJWCddSMWq+sBJ7muMJndFZabF7frZKwj3RGWeqVepdmjXleieEmczClbQZ4wdJ80CegJ4laq+Gvg68N425/6gqt5vk7+RRwnZN/k3s9jBYrubbap/TZffGDdOZABU9dOq2vhE/QVwx8mHZIwjZZFcn78T6WiufUya0ZWo7n7FqlwyXX5jDOlkDOAngd8teE6BT4uIAv9BVR8ruoiIPAI8AnDbRauyHBeqGnJXI0G1432E1zXwVH2HKXEoHKkozDBGiVsaABH5Q+BCzlPvU9Xfz855H+ni6sMFl3mdql4RkXPAEyLyNVX9TN6JmXF4DOCVry5bus2YEAM3QsJCU3qmqqadxZLOJ9wq49ug3jAa3NIAqOob2z0vIu8E3gK8QVtLZfeucSX7viQijwOvAXINgDG+XEnqVDWw6CM8wkZIeCmJrd7CMLrESbOAHgR+HvgBVd0qOGcacKq6nv38ZuAXT3JfY3SxPsKG0TtOmgX0AWCW1K3zBRF5FEBEbheRT2TnnAf+VES+CPwV8HFV/eQJ72uMOQIsOM8dvsR5H1E2YWbDODIn2gGo6ncWHL8CPJT9/BzwXSe5j2E0I8DLogrlrMtXUGXRRXw7rpk2j2EcAVMDNYaOMy6ikk3+wG6jnjuaev8ahnFrzAAYQ8e887kFY0LaBcwwjMNhBsAYOkIbWYhgicOGcWjMABhDx0pIDnRi00yfv1OaQYYxDpgaqDF0rIaEKXGcbmr7GIBL9Vo/h2UYQ4cZAGMoeTGps5zETDtHrMqGZf8YxpExA2AMLXXUWnIaxgmwGIBhGMaYYgbAMAxjTDEDYBiGMaaYATAMwxhTzAAYhmGMKWYADMMwxhQp6OEyEIjINeBbXbzFIrDcxeufBBvb8RjUsQ3quMDGdlwGdWwvV9XZw5w40HUAqnq2m9cXkSdV9YFu3uO42NiOx6CObVDHBTa24zKoYxORJw97rrmADMMwxhQzAIZhGGPKuBuAx/o9gDbY2I7HoI5tUMcFNrbjMqhjO/S4BjoIbBiGYXSPcd8BGIZhjC1mADJE5J+JiIrIYr/H0kBEfklEviQiXxCRT4vI7f0eUwMR+RUR+Vo2vsdFZKHfYwIQkb8nIl8RkSAiA5GhISIPisjTIvKMiLyn3+NpICIfEpElEflyv8fSiojcKSJ/LCJPZX/Pf9LvMQGIyISI/JWIfDEb1//W7zG1IiJeRP4/EfnYrc41A0D6zwa8Cfh2v8fSwq+o6qtV9X7gY8Av9HtATTwBvEpVXw18HXhvn8fT4MvA3wU+0++BQPphBH4d+GHgFcA7ROQV/R3VLr8JPNjvQRQQA/9UVf8G8L3AuwbkfasCP6Sq3wXcDzwoIt/b5zG18k+Apw5zohmAlH8D/HMYrH6CqrrW9HCaARqfqn5aVePs4V8Ad/RzPA1U9SlVfbrf42jiNcAzqvqcqtaA3wHe1ucxAaCqnwFW+j2OPFT1RVX9fPbzOumEdrG/owJN2cgelrKvgflcisgdwN8GfuMw54+9ARCRtwIvqOoX+z2WPETkfxeR54F/wGDtAJr5SeAP+j2IAeUi8HzT48sMwEQ2TIjIPcB3A3/Z35GkZC6WLwBLwBOqOhDjyvhV0sXsoVrkDXQlcKcQkT8ELuQ89T7gXwBv7u2I9mg3NlX9fVV9H/A+EXkv8G7g/YMytuyc95Fu1z88SOMaICTn2MCsGAcdEZkBfg/4uZYdcd9Q1QS4P4t7PS4ir1LVvsdRROQtwJKqfk5EXn+Y14yFAVDVN+YdF5G/CdwLfFFEIHVjfF5EXqOqL/VzbDn8NvBxemgAbjU2EXkn8BbgDdrDfOIjvGeDwGXgzqbHdwBX+jSWoUJESqST/4dV9b/0ezytqOpNEfnvpHGUvhsA4HXAW0XkIWACmBOR/0dV/+eiF4y1C0hV/1pVz6nqPap6D+mH9X/o1eR/K0TkvqaHbwW+1q+xtCIiDwI/D7xVVbf6PZ4B5rPAfSJyr4iUgYeBj/Z5TAOPpCuyDwJPqeq/7vd4GojI2UbGm4hMAm9kQD6XqvpeVb0jm8seBv6o3eQPY24AhoBfFpEvi8iXSN1UA5EKl/EBYBZ4IktTfbTfAwIQkR8VkcvA9wEfF5FP9XM8WaD83cCnSAOZ/1lVv9LPMTUQkY8Afw68XEQui8hP9XtMTbwO+HHgh7L/ry9kK9t+cxvwx9ln8rOkMYBbplsOKlYJbBiGMabYDsAwDGNMMQNgGIYxppgBMAzDGFPMABiGYYwpZgAMwzDGFDMAhmEYY4oZAMMwjDHFDIBhGMaY8v8Dgv2Yvl4mQ0gAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 获取数据值所在的范围，程序这段讲过，用来画图\n",
    "x_min, x_max = x_data[:, 0].min() - 1, x_data[:, 0].max() + 1\n",
    "y_min, y_max = x_data[:, 1].min() - 1, x_data[:, 1].max() + 1\n",
    "\n",
    "# 生成网格矩阵\n",
    "xx, yy = np.meshgrid(np.arange(x_min, x_max, 0.02),\n",
    "                     np.arange(y_min, y_max, 0.02))\n",
    "\n",
    "z = model.predict(np.c_[xx.ravel(), yy.ravel()])# ravel与flatten类似，多维数据转一维。flatten不会改变原始数据，ravel会改变原始数据\n",
    "z = z.reshape(xx.shape)\n",
    "# 等高线图\n",
    "cs = plt.contourf(xx, yy, z)\n",
    "# 样本散点图\n",
    "plt.scatter(x_data[:, 0], x_data[:, 1], c=y_data)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "             precision    recall  f1-score   support\n",
      "\n",
      "        0.0       1.00      1.00      1.00        47\n",
      "        1.0       1.00      1.00      1.00        53\n",
      "\n",
      "avg / total       1.00      1.00      1.00       100\n",
      "\n"
     ]
    }
   ],
   "source": [
    "predictions = model.predict(x_data)\n",
    "print(classification_report(predictions,y_data))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "anaconda-cloud": {},
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
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